1
00:00:03,120 --> 00:00:06,000
Speaker 1: Welcome to Stuff to Blow Your Mind from how Stuff

2
00:00:06,000 --> 00:00:14,160
Speaker 1: Works dot com. Hey, welcome to Stuff to Blow your Mind.

3
00:00:14,280 --> 00:00:17,400
Speaker 1: My name is Robert Lamb and I'm Julie Douglas. Julie,

4
00:00:17,400 --> 00:00:20,040
Speaker 1: I think most of our listeners probably tuned into our

5
00:00:20,079 --> 00:00:23,400
Speaker 1: episode but the future Shot, so they are probably familiar

6
00:00:23,440 --> 00:00:26,000
Speaker 1: with the concept. But even if they're not familiar with

7
00:00:26,040 --> 00:00:29,320
Speaker 1: the concept the future shot, I think they might feel

8
00:00:29,400 --> 00:00:32,160
Speaker 1: a little of it. Let's get through with this episode. Yeah,

9
00:00:32,200 --> 00:00:35,840
Speaker 1: because the toddlers in Future Shot talked about this idea

10
00:00:36,120 --> 00:00:38,960
Speaker 1: of having these sort of neural prosthetics. I didn't call

11
00:00:39,000 --> 00:00:42,600
Speaker 1: him that, but it's more like brain computer interfaces. And

12
00:00:42,640 --> 00:00:45,760
Speaker 1: this was a wild idea in the seventies. But hey,

13
00:00:45,840 --> 00:00:48,280
Speaker 1: let's let's get real here. I mean, this thing is happening.

14
00:00:48,320 --> 00:00:52,680
Speaker 1: In two thousand and three, the world's first brain prosthesis

15
00:00:53,400 --> 00:00:56,680
Speaker 1: was put into an animal, and then just this year

16
00:00:57,360 --> 00:01:00,200
Speaker 1: there was an auditory brain implant in a toddler. So

17
00:01:00,240 --> 00:01:03,240
Speaker 1: we can see that this is becoming more and more common.

18
00:01:03,880 --> 00:01:07,280
Speaker 1: And I attended a panel at the World Science Festival

19
00:01:07,480 --> 00:01:11,800
Speaker 1: on this very topic. It's called cells to silicon your brain.

20
00:01:11,840 --> 00:01:16,280
Speaker 1: And by the way, it always seems to be like

21
00:01:16,360 --> 00:01:18,760
Speaker 1: the the year that all this stuff is going to

22
00:01:18,840 --> 00:01:20,800
Speaker 1: be here for real. I don't know if you've noticed that,

23
00:01:21,440 --> 00:01:23,840
Speaker 1: but they talked about the steps there that they're taking

24
00:01:24,040 --> 00:01:27,520
Speaker 1: right now to make this more of a reality. And

25
00:01:27,560 --> 00:01:30,160
Speaker 1: we'll talk about that in a second. Yeah. Yeah, to

26
00:01:30,200 --> 00:01:33,600
Speaker 1: your point, those are the new two thousand's, I guess,

27
00:01:33,800 --> 00:01:36,360
Speaker 1: the years we can look to and say, this is

28
00:01:36,400 --> 00:01:38,640
Speaker 1: the technology we're gonna have, This is the life we're

29
00:01:38,640 --> 00:01:41,800
Speaker 1: gonna have because of these advancements. And to your point,

30
00:01:42,400 --> 00:01:45,319
Speaker 1: we've seen some pretty impressive steps made thus far. I mean,

31
00:01:45,360 --> 00:01:49,200
Speaker 1: the uh cyborg rat with the with the brain berth

32
00:01:49,240 --> 00:01:52,600
Speaker 1: thesis and cyborg Toddler. It's pretty great now not to

33
00:01:52,720 --> 00:01:54,440
Speaker 1: not to to get too caught up in the idea

34
00:01:54,440 --> 00:01:58,120
Speaker 1: of the cyborg, because if we've we've discussed before, technological

35
00:01:58,200 --> 00:02:02,040
Speaker 1: adaptations on your body, inside your body are becoming more

36
00:02:02,080 --> 00:02:04,720
Speaker 1: and more every day, you know, ranging from a pacemaker

37
00:02:04,720 --> 00:02:07,280
Speaker 1: to a risk watch, and so we're just seeing the

38
00:02:07,720 --> 00:02:12,640
Speaker 1: natural extrapolation of that idea. Yeah, you're augmenting your natural abilities, right.

39
00:02:12,680 --> 00:02:15,880
Speaker 1: I wear glasses, so I'm augmenting my vision. Yeah. None

40
00:02:15,919 --> 00:02:18,960
Speaker 1: of this makes anybody less human. All it does is

41
00:02:19,040 --> 00:02:22,799
Speaker 1: either makes up for injuries or or or some sort

42
00:02:22,800 --> 00:02:27,680
Speaker 1: of shortcomings in one's biology, or it is it is

43
00:02:27,840 --> 00:02:30,320
Speaker 1: gaming the system a bit, or maybe gaming the system

44
00:02:30,360 --> 00:02:33,480
Speaker 1: a lot, depending on what the augmentation is well. And

45
00:02:33,520 --> 00:02:35,920
Speaker 1: you would need this sort of gaming of the system

46
00:02:36,120 --> 00:02:38,480
Speaker 1: quite a bit if you were someone like I say,

47
00:02:38,560 --> 00:02:42,120
Speaker 1: stroke patient who have lost the ability to move your limbs.

48
00:02:42,680 --> 00:02:46,280
Speaker 1: And neuroscientists John Donohue was actually talking about this on

49
00:02:46,320 --> 00:02:51,000
Speaker 1: the panel. Specifically, he referenced a stroke patient named Cathy,

50
00:02:51,040 --> 00:02:54,079
Speaker 1: and he showed this film of her. She sustained permanent

51
00:02:54,160 --> 00:02:57,080
Speaker 1: damage to her brain and as a result, the axons

52
00:02:57,200 --> 00:02:59,799
Speaker 1: that run along her spine and deliver information from her

53
00:03:00,000 --> 00:03:03,960
Speaker 1: motor cortex or her limbs were incapacitated, and so she

54
00:03:04,040 --> 00:03:07,359
Speaker 1: was relegated to a life in a wheelchair with others

55
00:03:07,400 --> 00:03:10,679
Speaker 1: helping her to move and to feed her. And then

56
00:03:10,960 --> 00:03:14,160
Speaker 1: she signed on to this five year experiment with Donna

57
00:03:14,200 --> 00:03:17,600
Speaker 1: Hue and others other researchers, and there was an implant

58
00:03:17,680 --> 00:03:22,040
Speaker 1: inserted into her brain that interacted with robotic arms that

59
00:03:22,080 --> 00:03:26,119
Speaker 1: would do some things for her, like like get the coffee,

60
00:03:26,120 --> 00:03:28,560
Speaker 1: like actually bring coffee to her lips, which she hadn't

61
00:03:28,600 --> 00:03:32,120
Speaker 1: done for years. Yeah, in the video footage, of this

62
00:03:32,200 --> 00:03:34,320
Speaker 1: is pretty remarkable because, yeah, she has not in and

63
00:03:34,360 --> 00:03:37,200
Speaker 1: over a decade, she has not raised a cup of

64
00:03:37,240 --> 00:03:40,000
Speaker 1: coffee to her own lips, of her own volition, and

65
00:03:40,080 --> 00:03:43,280
Speaker 1: now she's doing it. She's thinking the robot arm into action.

66
00:03:43,560 --> 00:03:45,600
Speaker 1: It's there's nothing, you know, she's not moving anything with

67
00:03:45,600 --> 00:03:48,480
Speaker 1: their tongue, it's nothing with their muscles. It's all inside

68
00:03:48,640 --> 00:03:51,560
Speaker 1: her mind. She's thinking about the movement and it is happening,

69
00:03:51,880 --> 00:03:54,400
Speaker 1: and it's amazing. It's it's it's the stuff that you

70
00:03:54,440 --> 00:03:56,240
Speaker 1: would often think, even today, you tend to think of

71
00:03:56,320 --> 00:03:59,320
Speaker 1: as science fiction, the idea that I'm commanding a machine

72
00:03:59,400 --> 00:04:01,920
Speaker 1: with my mind, I'm moving something other than my physical

73
00:04:02,000 --> 00:04:04,360
Speaker 1: body with my mind, and yet here it is. Yeah,

74
00:04:04,440 --> 00:04:07,000
Speaker 1: it is amazing. And so how do you get your

75
00:04:07,040 --> 00:04:10,600
Speaker 1: thoughts externally out there to do something for you. Well,

76
00:04:10,640 --> 00:04:12,840
Speaker 1: it turns out that what you do is you insert

77
00:04:12,920 --> 00:04:16,320
Speaker 1: a wire into the r motor cortex. And what this

78
00:04:16,400 --> 00:04:18,839
Speaker 1: wire is doing, it's just it's not a very big wire,

79
00:04:18,880 --> 00:04:21,040
Speaker 1: so it's not like she's got things, you know, sticking

80
00:04:21,040 --> 00:04:23,440
Speaker 1: out of her head. But what it's doing is it's

81
00:04:23,640 --> 00:04:27,920
Speaker 1: fishing for neural spikes. Now, what are neural spikes? Okay,

82
00:04:28,400 --> 00:04:32,080
Speaker 1: every time neurons fire in the brain, they're shuttling ions

83
00:04:32,160 --> 00:04:35,280
Speaker 1: back and forth, and there's an electrical potential change there.

84
00:04:35,839 --> 00:04:38,040
Speaker 1: So if you have a wire inserted into the brain

85
00:04:38,120 --> 00:04:42,920
Speaker 1: that can detect that electrical change and when it does, uh,

86
00:04:42,960 --> 00:04:45,279
Speaker 1: you know, changes in the ions when you're on stir

87
00:04:45,320 --> 00:04:51,159
Speaker 1: about and meat Uh, it will actually transmit information out

88
00:04:51,400 --> 00:04:55,279
Speaker 1: to a computer to say, I detect neural activity here

89
00:04:55,400 --> 00:04:58,240
Speaker 1: right now, and it's measuring that. So we've talked about

90
00:04:58,279 --> 00:05:02,320
Speaker 1: this before with the Jennifer anistone neurons. I think that's

91
00:05:02,360 --> 00:05:05,840
Speaker 1: what we called them. That when someone is thinking about

92
00:05:05,920 --> 00:05:09,480
Speaker 1: Jennifer Anderson, some people I which I should say not everybody,

93
00:05:10,040 --> 00:05:13,000
Speaker 1: you will see a spike in neuronal activity because there's

94
00:05:13,000 --> 00:05:16,960
Speaker 1: a whole database of information and memory they are related

95
00:05:17,000 --> 00:05:21,159
Speaker 1: to Jennifer Aniston and so the signal is really very strong. Well,

96
00:05:21,240 --> 00:05:24,280
Speaker 1: the same thing happens when you're thinking and you're concentrating,

97
00:05:24,680 --> 00:05:26,800
Speaker 1: like I would like to move my arm right now,

98
00:05:26,839 --> 00:05:29,960
Speaker 1: and I'm trying to think about moving my arm. All

99
00:05:30,000 --> 00:05:33,360
Speaker 1: of those neurons which sort of coalesced together to give

100
00:05:33,360 --> 00:05:35,680
Speaker 1: off a strong signal, and that's what that wire could detect,

101
00:05:36,080 --> 00:05:39,360
Speaker 1: identifying what that signal looks like and then feeding it

102
00:05:39,400 --> 00:05:43,080
Speaker 1: out to this machine. I I often in looking at

103
00:05:43,080 --> 00:05:45,120
Speaker 1: this topic, I keep thinking of it in terms of

104
00:05:45,120 --> 00:05:49,080
Speaker 1: the road, like a mountain road. The early examples we

105
00:05:49,120 --> 00:05:52,839
Speaker 1: talked about that the rat prosthesis that had to do

106
00:05:52,880 --> 00:05:57,560
Speaker 1: with the damaged hippocampus, or the the toddler um prosthesis,

107
00:05:57,760 --> 00:06:01,320
Speaker 1: which specifically was an auditory brain stem and plant. These

108
00:06:01,360 --> 00:06:04,080
Speaker 1: are cases where you have point A, point B, and

109
00:06:04,160 --> 00:06:06,520
Speaker 1: point C. All right, you know you have a road,

110
00:06:06,720 --> 00:06:09,159
Speaker 1: imagine a road across the mountains from your house to

111
00:06:09,200 --> 00:06:12,400
Speaker 1: the store. But then that middle point is is a

112
00:06:12,400 --> 00:06:15,239
Speaker 1: part of the road that's washed away by by a flood.

113
00:06:15,520 --> 00:06:17,760
Speaker 1: So what do you do? How do you get from

114
00:06:17,800 --> 00:06:20,920
Speaker 1: A to to C? Well, you put a bridge over it, right,

115
00:06:21,000 --> 00:06:23,480
Speaker 1: And that's what a lot of this technology is figuring

116
00:06:23,520 --> 00:06:27,000
Speaker 1: out how to fill in that that gap, to how

117
00:06:27,000 --> 00:06:30,120
Speaker 1: to bridge that gap from from one from a healthy

118
00:06:30,160 --> 00:06:32,520
Speaker 1: part of the brain to another healthy part of well,

119
00:06:32,560 --> 00:06:35,040
Speaker 1: let's just say, a healthy part of the chain of

120
00:06:35,040 --> 00:06:38,800
Speaker 1: of of action and reaction to to bridge that gap,

121
00:06:39,160 --> 00:06:41,280
Speaker 1: you know, in a way that lets you function again.

122
00:06:41,720 --> 00:06:44,200
Speaker 1: And then in this we see we see a situation

123
00:06:44,240 --> 00:06:46,880
Speaker 1: where we're not only bridging the gap. We're building the

124
00:06:46,880 --> 00:06:51,679
Speaker 1: bridge out of the organism itself into an external system,

125
00:06:51,720 --> 00:06:54,640
Speaker 1: an external robotic system. You're right, because in this case,

126
00:06:54,680 --> 00:06:59,000
Speaker 1: point A is the neuronal activity. Point B would normally

127
00:06:59,000 --> 00:07:01,960
Speaker 1: be those acts on in her spine, but because they

128
00:07:02,000 --> 00:07:05,440
Speaker 1: can't send the signals to the body, what we're doing

129
00:07:05,480 --> 00:07:08,400
Speaker 1: here are what researchers are doing is they're taking those

130
00:07:08,440 --> 00:07:11,960
Speaker 1: signals out externally, out of the organism, as you say,

131
00:07:12,000 --> 00:07:16,040
Speaker 1: into the computer to decode that neuronal activity, and then

132
00:07:16,080 --> 00:07:18,920
Speaker 1: points C would be the robotic arms instead of her

133
00:07:18,960 --> 00:07:22,360
Speaker 1: own arms. And what is so amazing, so fascinating about

134
00:07:22,400 --> 00:07:25,160
Speaker 1: this is that what they're doing is that they're taking

135
00:07:25,240 --> 00:07:30,960
Speaker 1: the sounds that that these neuronal spikes made and they're

136
00:07:31,000 --> 00:07:34,840
Speaker 1: decoding them. So you might have like five blips that

137
00:07:34,840 --> 00:07:38,480
Speaker 1: that really mean move the arm to the right. You

138
00:07:38,560 --> 00:07:41,400
Speaker 1: might have seven blips that are decoded as move the

139
00:07:41,560 --> 00:07:45,600
Speaker 1: arm to the left, and that is then being put

140
00:07:45,640 --> 00:07:49,720
Speaker 1: into the robotic arm as a command. So the fact

141
00:07:49,720 --> 00:07:52,880
Speaker 1: that they can even take the information out and decode

142
00:07:52,920 --> 00:07:55,640
Speaker 1: it in a way that makes sense um and then

143
00:07:55,880 --> 00:07:58,840
Speaker 1: obviously matches up to what Cathy is thinking because she

144
00:07:58,880 --> 00:08:02,040
Speaker 1: can corroborate that. Yeah, you really get the sense. It's

145
00:08:02,040 --> 00:08:04,480
Speaker 1: almost like listening to a conversation through a wall, and

146
00:08:04,520 --> 00:08:07,600
Speaker 1: they're making sense of it through uh in sort of

147
00:08:07,640 --> 00:08:10,600
Speaker 1: indirect ways, but they're getting enough information out of it.

148
00:08:10,640 --> 00:08:13,480
Speaker 1: They're getting they're they're getting enough of the points they

149
00:08:13,520 --> 00:08:16,960
Speaker 1: get their high highlighting those neural spikes and then figuring

150
00:08:16,960 --> 00:08:20,560
Speaker 1: out what it's it's asking for and then reproducing it. Now,

151
00:08:20,560 --> 00:08:23,280
Speaker 1: the crazy thing about this, it's that they are getting

152
00:08:23,320 --> 00:08:26,280
Speaker 1: this neuronal activity from just a sampling of the neurons.

153
00:08:26,280 --> 00:08:29,240
Speaker 1: So maybe about fifty neurons out of the billions of

154
00:08:29,320 --> 00:08:33,480
Speaker 1: neurons in your brain, uh or the billions of synapsis.

155
00:08:33,480 --> 00:08:37,960
Speaker 1: So that's I mean, that's tiny, that tiny bit of

156
00:08:38,040 --> 00:08:40,720
Speaker 1: neural activity can be picked up and then acted upon.

157
00:08:41,200 --> 00:08:43,240
Speaker 1: I believe in the in the in they talk to you

158
00:08:43,200 --> 00:08:47,000
Speaker 1: you attended World Science Festial Robert crow which Um compared

159
00:08:47,080 --> 00:08:50,079
Speaker 1: it to to voting, where you have like a small

160
00:08:50,240 --> 00:08:52,679
Speaker 1: portion of the population that's voting and the rest are

161
00:08:52,720 --> 00:08:55,480
Speaker 1: not voting, but you're you're figuring out what the public

162
00:08:55,920 --> 00:08:58,880
Speaker 1: wants as a whole based on this small sample. And

163
00:08:58,880 --> 00:09:02,280
Speaker 1: that's kind of what's happened here with the with this situation. Yeah,

164
00:09:02,360 --> 00:09:05,079
Speaker 1: definitely check this out. You can go to RORLD Science

165
00:09:05,160 --> 00:09:08,120
Speaker 1: Festival to their website and just look for cells to

166
00:09:08,200 --> 00:09:11,640
Speaker 1: Silicon and you can see this talk. It's really great. Um,

167
00:09:11,760 --> 00:09:14,720
Speaker 1: so there are drawbacks this of course. There's the idea

168
00:09:14,840 --> 00:09:16,720
Speaker 1: that it may not be something that you could have

169
00:09:16,800 --> 00:09:18,760
Speaker 1: in your brain long term. Kathy has had it for

170
00:09:18,800 --> 00:09:20,800
Speaker 1: five years. She hasn't had any problems. But we know

171
00:09:20,840 --> 00:09:24,600
Speaker 1: that the body doesn't like foreign objects in it. That's true. Also,

172
00:09:24,600 --> 00:09:27,040
Speaker 1: it's worth noting that the robotic reach and grasp actions

173
00:09:27,080 --> 00:09:28,800
Speaker 1: are not going to be as fast as or as

174
00:09:28,840 --> 00:09:30,920
Speaker 1: accurate as those of the enable bodied person. I think

175
00:09:30,960 --> 00:09:33,240
Speaker 1: that's that's that should be pretty obvious. We're not at

176
00:09:33,240 --> 00:09:34,800
Speaker 1: the point in the technology where it's going to be

177
00:09:34,840 --> 00:09:37,960
Speaker 1: a one for one, but but it's it's a step

178
00:09:38,040 --> 00:09:41,440
Speaker 1: in that direction. Yeah, and uh. Donny Hue says that

179
00:09:41,960 --> 00:09:44,600
Speaker 1: the neural spikes are not always the same from trial

180
00:09:44,720 --> 00:09:47,240
Speaker 1: to trial. In other words, there's five blips, might be

181
00:09:47,400 --> 00:09:49,560
Speaker 1: six flips the next time or seven, and so they

182
00:09:49,600 --> 00:09:52,080
Speaker 1: have to figure out the pattern each time. And he

183
00:09:52,240 --> 00:09:55,440
Speaker 1: said that it's very likely that those neural spikes are

184
00:09:55,480 --> 00:09:58,360
Speaker 1: influenced by other conditions at the time, so it could

185
00:09:58,400 --> 00:10:01,079
Speaker 1: be hunger, it could be a particularly emotional state or

186
00:10:01,240 --> 00:10:05,920
Speaker 1: a physical state. UM, which is all really fascinating because

187
00:10:05,960 --> 00:10:08,600
Speaker 1: we've talked about a propri reception before, and we talked

188
00:10:08,600 --> 00:10:12,040
Speaker 1: about it and people who have lost the ability to

189
00:10:12,120 --> 00:10:16,040
Speaker 1: move their limbs and um, and how all of this

190
00:10:16,240 --> 00:10:20,800
Speaker 1: influences the body, the physical state of the body. So um.

191
00:10:20,800 --> 00:10:24,560
Speaker 1: Obviously it's not something that is perfect right now, but

192
00:10:24,720 --> 00:10:27,800
Speaker 1: it's the beginnings of something that could be really important,

193
00:10:27,960 --> 00:10:31,440
Speaker 1: that kind of neural pixie dust of the future. And

194
00:10:31,440 --> 00:10:35,760
Speaker 1: we'll get to that later. Now. Another fascinating example of

195
00:10:35,760 --> 00:10:38,119
Speaker 1: this technology can be found in the work of HeLa Nuremberg,

196
00:10:38,920 --> 00:10:44,240
Speaker 1: who is a neuroscientist working on technology for a prosthetic

197
00:10:44,320 --> 00:10:47,120
Speaker 1: I yeah, because she says that more than ten million

198
00:10:47,160 --> 00:10:50,200
Speaker 1: people in the US are blind or facing blindness because

199
00:10:50,240 --> 00:10:55,760
Speaker 1: of diseases like macular degeneration, and for the vast majority

200
00:10:55,800 --> 00:10:58,920
Speaker 1: of them, it's the prosthetic devices that are available right

201
00:10:58,960 --> 00:11:01,360
Speaker 1: now that are their best hope, but the ones that

202
00:11:01,640 --> 00:11:04,960
Speaker 1: that are common right now are not necessarily the best.

203
00:11:05,080 --> 00:11:11,240
Speaker 1: And she has created a neural prosthetic device that is amazing. Yeah.

204
00:11:11,360 --> 00:11:13,560
Speaker 1: Now think back again to that analogy I'm made about

205
00:11:13,600 --> 00:11:16,080
Speaker 1: the road point A, point B. In point C and

206
00:11:16,200 --> 00:11:19,320
Speaker 1: point B that middle point is washed out by a flood.

207
00:11:19,320 --> 00:11:20,680
Speaker 1: How do you get across it? How do you build

208
00:11:20,679 --> 00:11:23,240
Speaker 1: that bridge? So, when when you're looking at something just

209
00:11:23,360 --> 00:11:25,959
Speaker 1: with normal vision, normal healthy vision, you have the image

210
00:11:26,000 --> 00:11:27,960
Speaker 1: that's going to your eye, it's hitting your retina and

211
00:11:27,960 --> 00:11:30,079
Speaker 1: then the middleman that point B that it is the

212
00:11:30,360 --> 00:11:34,360
Speaker 1: retinal circuitry, and this essentially extracts information from it and

213
00:11:34,400 --> 00:11:37,400
Speaker 1: converts it into a code. And these are electrical pulses

214
00:11:37,400 --> 00:11:39,959
Speaker 1: that are sent to the brain. So to your point,

215
00:11:40,040 --> 00:11:42,200
Speaker 1: what do you do when the photo receptors die? What

216
00:11:42,240 --> 00:11:45,400
Speaker 1: do you do when one link in the chain vanishes? Yeah,

217
00:11:45,400 --> 00:11:48,920
Speaker 1: because that's what happens with macular degeneration. Those photoreceptors on

218
00:11:48,960 --> 00:11:51,520
Speaker 1: the retina die off, and then over time all the

219
00:11:51,559 --> 00:11:53,839
Speaker 1: other cells and the circuits that are connected to them

220
00:11:53,880 --> 00:11:58,360
Speaker 1: die off to renderingly you effectively blind. So the only

221
00:11:58,400 --> 00:12:01,679
Speaker 1: thing that you're left with are those put cells, and

222
00:12:01,720 --> 00:12:03,680
Speaker 1: those are the ones that send the signals to the brain.

223
00:12:03,720 --> 00:12:06,240
Speaker 1: But because of all that degeneration, they're not sending any

224
00:12:06,280 --> 00:12:10,280
Speaker 1: signals anymore. And if you have a regular prosthetic for eyesight,

225
00:12:11,000 --> 00:12:14,160
Speaker 1: um you could allow a person to see bright lights

226
00:12:14,240 --> 00:12:17,360
Speaker 1: or high contrast edges, but these are shadowy with like

227
00:12:17,520 --> 00:12:20,720
Speaker 1: really no real detail. And what you're using in this

228
00:12:20,880 --> 00:12:24,800
Speaker 1: regular prosthetic for eyesight is an encoder that takes in light,

229
00:12:25,000 --> 00:12:28,240
Speaker 1: but the firing pattern is all over the place. So

230
00:12:28,280 --> 00:12:30,600
Speaker 1: what you need then is a device that can mimic

231
00:12:30,640 --> 00:12:33,560
Speaker 1: the actions of that the front end circuitry of sight

232
00:12:34,000 --> 00:12:37,440
Speaker 1: send signals to the retina's output cells and and and

233
00:12:37,480 --> 00:12:39,800
Speaker 1: in this you're you end up mimicking the actions of

234
00:12:39,840 --> 00:12:42,760
Speaker 1: that front end circuitry, so you're taking the image converting

235
00:12:42,800 --> 00:12:46,160
Speaker 1: them into the retina's own code. Yeah. What Nuremberg has

236
00:12:46,160 --> 00:12:49,600
Speaker 1: created is um this prosthetic device that has two parts,

237
00:12:49,960 --> 00:12:53,360
Speaker 1: an encoder and a transducer. Now that encoder mimics the

238
00:12:53,360 --> 00:12:56,400
Speaker 1: actions that you say, that front end circuitry, and then

239
00:12:56,440 --> 00:12:59,600
Speaker 1: the transducer makes the output cells send the code to

240
00:12:59,640 --> 00:13:02,880
Speaker 1: the sin and the result is this retinal prosthetic that

241
00:13:02,920 --> 00:13:07,320
Speaker 1: can produce normal retinal output. And we're talking about is

242
00:13:07,320 --> 00:13:10,840
Speaker 1: is a set of equations that they can implement on

243
00:13:10,880 --> 00:13:14,120
Speaker 1: a chip. And so she's saying, hey, it's just math here.

244
00:13:14,360 --> 00:13:17,360
Speaker 1: And that's the exciting part is that if you take

245
00:13:17,400 --> 00:13:20,840
Speaker 1: these two components of the prosthetic device, then you can

246
00:13:20,920 --> 00:13:24,960
Speaker 1: mimic real eyesight in a person who is blind. Yeah,

247
00:13:24,960 --> 00:13:28,320
Speaker 1: it's pretty pretty, pretty phenomenal. And we're talking about that

248
00:13:28,400 --> 00:13:30,400
Speaker 1: the individual would wear a sort of camera that has

249
00:13:30,440 --> 00:13:33,120
Speaker 1: an encoder of a device that's taking that information from

250
00:13:33,200 --> 00:13:35,959
Speaker 1: the camera and translating it into the retinas code, translating

251
00:13:36,000 --> 00:13:38,720
Speaker 1: it into a version that the brain can make sense of.

252
00:13:39,640 --> 00:13:45,119
Speaker 1: You're talking about a real direct link between mechanical artificial

253
00:13:45,360 --> 00:13:49,960
Speaker 1: sensory and organic reasoning of that sense information. Now, to

254
00:13:50,040 --> 00:13:52,080
Speaker 1: get a sense of this, you should definitely check out

255
00:13:52,120 --> 00:13:55,680
Speaker 1: the talk because she shows two different examples of how

256
00:13:55,720 --> 00:13:59,240
Speaker 1: this works with a regular prosthetic device and with this

257
00:13:59,320 --> 00:14:03,080
Speaker 1: new one. And what she shows is sort of a

258
00:14:03,160 --> 00:14:08,280
Speaker 1: readout of the neural firing activity and regular eyesight um

259
00:14:08,440 --> 00:14:10,720
Speaker 1: the regular prosthetic and the new one. And you will

260
00:14:10,760 --> 00:14:14,480
Speaker 1: see that in the new prosthetic and regular eyes eyesight,

261
00:14:14,600 --> 00:14:19,840
Speaker 1: those patterns are almost identical. It's not perfect, but that

262
00:14:19,960 --> 00:14:22,080
Speaker 1: middle part, the regular one right now that just has

263
00:14:22,120 --> 00:14:25,720
Speaker 1: the encoder that just takes in light, is very imperfect.

264
00:14:26,160 --> 00:14:28,520
Speaker 1: And then she gives the second example of she said,

265
00:14:28,560 --> 00:14:30,800
Speaker 1: you know, we wanted to take a snapshot of what

266
00:14:30,880 --> 00:14:34,600
Speaker 1: people were seeing or what these primates were seeing, because

267
00:14:34,600 --> 00:14:38,600
Speaker 1: I believe they're in human trials right now, clinical trials.

268
00:14:38,600 --> 00:14:42,600
Speaker 1: But what those um images that we're seeing. You see

269
00:14:42,880 --> 00:14:46,000
Speaker 1: the regular eyesight, you see a baby's face. It's perfectly rendered,

270
00:14:46,000 --> 00:14:48,320
Speaker 1: of course, right because you have perfect eyesight. Then you

271
00:14:48,360 --> 00:14:51,600
Speaker 1: have the regular prosthetic and it's very shadowy. You can't

272
00:14:51,640 --> 00:14:54,680
Speaker 1: even make out the pattern, which is amazing because you know,

273
00:14:54,720 --> 00:14:58,200
Speaker 1: you and I have discussed before how humans are pattern

274
00:14:58,280 --> 00:15:01,520
Speaker 1: recognition machines and yet there's no not enough data points

275
00:15:01,560 --> 00:15:04,240
Speaker 1: here to really make a clear picture. And then you

276
00:15:04,280 --> 00:15:06,480
Speaker 1: have this new device that she came up with, and

277
00:15:06,520 --> 00:15:08,960
Speaker 1: it is plain as day that that is the baby space.

278
00:15:09,520 --> 00:15:14,360
Speaker 1: It is not exact, but can you imagine being someone

279
00:15:14,400 --> 00:15:18,120
Speaker 1: who has lost their sight and you all of a

280
00:15:18,160 --> 00:15:22,000
Speaker 1: sudden are hooked up with this neural prosthetic device for

281
00:15:22,040 --> 00:15:26,160
Speaker 1: your eye and you're able to see something is I

282
00:15:26,160 --> 00:15:29,120
Speaker 1: don't know, iconic and recognizable as a tree or a

283
00:15:29,120 --> 00:15:31,720
Speaker 1: baby space. Again, yeah, I mean you would have have

284
00:15:31,880 --> 00:15:34,320
Speaker 1: some level of detail as well as just you know,

285
00:15:34,360 --> 00:15:39,080
Speaker 1: an abstract idea of your environment just for navigational purposes.

286
00:15:39,120 --> 00:15:42,200
Speaker 1: So it's pretty pretty amazing stuff. Yeah, And as you

287
00:15:42,280 --> 00:15:45,040
Speaker 1: had said, this sort of washed out areas, um, you know,

288
00:15:45,120 --> 00:15:47,840
Speaker 1: from points A to C. This is something that Nuremberg

289
00:15:47,960 --> 00:15:50,200
Speaker 1: is is imagining for the rest of the brains, and

290
00:15:50,320 --> 00:15:53,040
Speaker 1: not just eyesight, but for stroke victims. So in the

291
00:15:53,080 --> 00:15:56,440
Speaker 1: cortex of trying to create this sort of communications center

292
00:15:56,640 --> 00:15:59,680
Speaker 1: using the same sort of device. Yeah, figuring any any

293
00:15:59,760 --> 00:16:02,640
Speaker 1: kind of situation where there's a there's a gap, where

294
00:16:02,640 --> 00:16:04,520
Speaker 1: there's a missing link in the chain or a damage

295
00:16:04,600 --> 00:16:07,360
Speaker 1: link in the chain, then we could conceivably go in

296
00:16:07,400 --> 00:16:11,800
Speaker 1: there and build that that bridge, that neural bridge from

297
00:16:11,840 --> 00:16:15,040
Speaker 1: point A to point C. All Right, we're gonna take

298
00:16:15,040 --> 00:16:16,480
Speaker 1: a quick break, and when we get back, we're going

299
00:16:16,520 --> 00:16:18,520
Speaker 1: to talk about it. Just sort of neural dust that

300
00:16:18,600 --> 00:16:21,520
Speaker 1: we may all have sprinkled into our brains in the year.

301
00:16:31,680 --> 00:16:34,480
Speaker 1: All right, we're back. Uh So, if you were not

302
00:16:34,600 --> 00:16:38,000
Speaker 1: future shocked by the earlier information about sort of the

303
00:16:38,040 --> 00:16:42,000
Speaker 1: current state in the in the depending state um of

304
00:16:42,160 --> 00:16:45,600
Speaker 1: these these neural enhancements, then hold onto your bridges, because

305
00:16:45,640 --> 00:16:49,480
Speaker 1: we're about to look at where this could go, where

306
00:16:49,520 --> 00:16:52,680
Speaker 1: this is going, and how it could dramatically change not

307
00:16:52,720 --> 00:16:54,840
Speaker 1: only our lives, but but but what it is to

308
00:16:54,840 --> 00:16:58,000
Speaker 1: be human. Yeah, Okay, so think back to Kathy and

309
00:16:58,120 --> 00:17:00,560
Speaker 1: think about that wire in her brain time I believe

310
00:17:00,680 --> 00:17:04,840
Speaker 1: is referred to as brain gate. UM. Michael Maharbits has

311
00:17:04,920 --> 00:17:08,800
Speaker 1: created a new kind of prosthetic in the form of

312
00:17:08,840 --> 00:17:12,960
Speaker 1: these cubes as small as neurons. Okay, we're talking about

313
00:17:13,000 --> 00:17:17,960
Speaker 1: fifty microns across. Now. The thing is is that they

314
00:17:18,040 --> 00:17:22,880
Speaker 1: don't um they don't work with electromagnetic frequency because they're

315
00:17:22,880 --> 00:17:25,840
Speaker 1: too small, so you can't have something like a wire

316
00:17:25,960 --> 00:17:29,240
Speaker 1: detecting them. But you can use sonogram to detect these.

317
00:17:30,160 --> 00:17:33,120
Speaker 1: And what they're thinking is that you could have these

318
00:17:33,160 --> 00:17:36,320
Speaker 1: sprinkled throughout your brain and you would have all these

319
00:17:36,400 --> 00:17:41,120
Speaker 1: different data points measuring that neuronal activity, and you could

320
00:17:41,119 --> 00:17:45,840
Speaker 1: have a far more robust system that not only exports

321
00:17:45,880 --> 00:17:48,040
Speaker 1: the sort of neural activity, the sort of thoughts that

322
00:17:48,119 --> 00:17:53,280
Speaker 1: you're having, that maybe even imports information. So when you

323
00:17:53,320 --> 00:17:56,600
Speaker 1: start to think about this, this neural dust as it's called,

324
00:17:57,720 --> 00:18:00,600
Speaker 1: and you start to see how you could have these

325
00:18:00,640 --> 00:18:04,480
Speaker 1: prosthetics not just external to you, but implanted in you,

326
00:18:04,680 --> 00:18:08,840
Speaker 1: and you could have this this really very uh sophisticated

327
00:18:09,080 --> 00:18:12,840
Speaker 1: system of information coming in and going out of your body,

328
00:18:12,840 --> 00:18:15,720
Speaker 1: out of your brain. Yeah, I mean, we're talking about

329
00:18:15,720 --> 00:18:19,200
Speaker 1: the ability to to have more of a hands on,

330
00:18:19,720 --> 00:18:23,639
Speaker 1: real time idea of what the brain is doing just

331
00:18:24,119 --> 00:18:26,080
Speaker 1: throughout the course of the day, not just when you're

332
00:18:26,080 --> 00:18:29,320
Speaker 1: hooked up to some machines or or your run through

333
00:18:29,480 --> 00:18:32,960
Speaker 1: an fMRI. I. This would be like a daily constant

334
00:18:33,080 --> 00:18:36,119
Speaker 1: understanding of what your brain is doing. Yeah, and I

335
00:18:36,119 --> 00:18:38,159
Speaker 1: mean you you see this. That the fact that you

336
00:18:38,160 --> 00:18:41,320
Speaker 1: could have more of this neural dust spread throughout your

337
00:18:41,320 --> 00:18:45,560
Speaker 1: brain again, more data points of collection, more ability to

338
00:18:45,680 --> 00:18:48,560
Speaker 1: import that data. But not only that, you could have

339
00:18:48,680 --> 00:18:51,520
Speaker 1: this stuff in there maybe for the rest of your life,

340
00:18:51,520 --> 00:18:53,520
Speaker 1: because as we discussed before, the brain is not like

341
00:18:53,520 --> 00:18:56,200
Speaker 1: foreign objects. But if you have something as tiny as

342
00:18:56,240 --> 00:18:59,199
Speaker 1: a neuron, it's very possible that it could pass in

343
00:18:59,240 --> 00:19:03,040
Speaker 1: the body as something that's uh supposed to be there. Okay,

344
00:19:03,040 --> 00:19:06,399
Speaker 1: so at conceivably then at even an early age, you

345
00:19:06,440 --> 00:19:10,000
Speaker 1: are injected. Your brain is injected with this dust, and

346
00:19:10,040 --> 00:19:12,720
Speaker 1: this dust is spread out through the brain and it's

347
00:19:12,760 --> 00:19:18,360
Speaker 1: all reporting and receiving information from the outside. Well. Right,

348
00:19:18,440 --> 00:19:22,440
Speaker 1: So for someone like Cathy for instance, um, she would

349
00:19:22,480 --> 00:19:24,840
Speaker 1: get sort of an upgrade here because again let's assume

350
00:19:24,880 --> 00:19:27,720
Speaker 1: that there's some sort of implant here or a prosthetic

351
00:19:27,720 --> 00:19:31,600
Speaker 1: device that could easily allow her to to move her

352
00:19:31,640 --> 00:19:37,640
Speaker 1: body around um, and maybe even she could receive directions UM.

353
00:19:37,680 --> 00:19:42,240
Speaker 1: And that's where this thing gets really weird, because you

354
00:19:42,359 --> 00:19:46,560
Speaker 1: could effectively use this as a brain upgrade. And we

355
00:19:46,640 --> 00:19:48,119
Speaker 1: talked about this before, and I think it was in

356
00:19:48,240 --> 00:19:52,480
Speaker 1: monkeys that they had their neural path at ways tinkered

357
00:19:52,520 --> 00:19:55,159
Speaker 1: with when they were making decisions, and the idea was

358
00:19:55,280 --> 00:19:59,159
Speaker 1: you could increase neural activity in one direction so that

359
00:19:59,200 --> 00:20:02,800
Speaker 1: they made spec scific um movements or decisions about what

360
00:20:02,800 --> 00:20:05,639
Speaker 1: they were about to do the right decision, right. So

361
00:20:05,880 --> 00:20:08,639
Speaker 1: what if you have this neural pixie dust and it

362
00:20:08,680 --> 00:20:12,440
Speaker 1: could bolster your memory, It could create stronger neural pathways

363
00:20:12,560 --> 00:20:15,560
Speaker 1: to information. I mean, you wouldn't have to study so much, right.

364
00:20:16,800 --> 00:20:19,840
Speaker 1: It's it's pretty fascinating because we're talking about not only

365
00:20:20,480 --> 00:20:23,480
Speaker 1: not only using this technology to bridge gaps and to

366
00:20:23,760 --> 00:20:26,800
Speaker 1: service a bridge where an area was damaged, but just

367
00:20:26,880 --> 00:20:30,359
Speaker 1: to speed up the connections, to improve the the the

368
00:20:30,440 --> 00:20:33,560
Speaker 1: overall road to where you have you know, just imagine

369
00:20:33,600 --> 00:20:36,280
Speaker 1: again to use the analogy of the mountain mountain roads

370
00:20:36,320 --> 00:20:38,840
Speaker 1: where you have lots of bridges, not because the the

371
00:20:39,560 --> 00:20:44,040
Speaker 1: road is unsafe, but because this makes the transportation, faster, smoother,

372
00:20:44,600 --> 00:20:48,040
Speaker 1: uh and uh and and altogether more effective. Right, It's

373
00:20:48,080 --> 00:20:49,960
Speaker 1: a kind of true all because not only would it

374
00:20:50,000 --> 00:20:53,720
Speaker 1: be helpful for say, struck victims or someone with you know,

375
00:20:53,720 --> 00:20:58,320
Speaker 1: immacular degeneration in this case, UM, but because conceivably it

376
00:20:58,320 --> 00:20:59,920
Speaker 1: could work in the same way that nurembergers to be

377
00:21:00,000 --> 00:21:03,640
Speaker 1: sing her system. But yeah, it could just give you

378
00:21:03,760 --> 00:21:07,040
Speaker 1: that sort of lift that everybody has been fantasizing about

379
00:21:07,119 --> 00:21:10,359
Speaker 1: and thinking about in terms of upgrading your brain in

380
00:21:10,400 --> 00:21:13,360
Speaker 1: a way that it runs faster and smoother. As you say, yeah,

381
00:21:13,359 --> 00:21:16,040
Speaker 1: and you could have like a daily or weekly email

382
00:21:16,080 --> 00:21:18,040
Speaker 1: that you received telling you what you're almost like you

383
00:21:18,080 --> 00:21:20,320
Speaker 1: would web traffic for your website, except you would have

384
00:21:20,320 --> 00:21:22,280
Speaker 1: a readout saying, hey, this is how your brain is doing.

385
00:21:22,359 --> 00:21:25,400
Speaker 1: This is your your neural activity for a given day,

386
00:21:25,400 --> 00:21:28,120
Speaker 1: a given week. Yeah. And here's the thing again, this

387
00:21:28,200 --> 00:21:30,960
Speaker 1: is that we're not just talking about um output here

388
00:21:31,880 --> 00:21:35,760
Speaker 1: working with an implanted prosthetic devices, but we're also talking

389
00:21:35,760 --> 00:21:40,840
Speaker 1: about input. And while this doesn't work on an electromagnetic frequency,

390
00:21:40,960 --> 00:21:43,600
Speaker 1: it does work on that that sort of ultrasound. In fact,

391
00:21:43,640 --> 00:21:48,120
Speaker 1: these neural pixie dusts are called tuning forks for ultrasound. Uh,

392
00:21:48,160 --> 00:21:51,800
Speaker 1: the idea is that maybe you could still kind of

393
00:21:51,840 --> 00:21:54,920
Speaker 1: hack into this system, and that would be a problem, right,

394
00:21:54,960 --> 00:21:58,920
Speaker 1: you could hack into someone's neural circuitry and have them

395
00:21:58,960 --> 00:22:01,119
Speaker 1: doing things. So, so you don't mean just in the

396
00:22:01,160 --> 00:22:04,640
Speaker 1: sense of life hacking where I'm improving my brain by

397
00:22:04,640 --> 00:22:06,920
Speaker 1: having the neural pixie dust. You're talking about an outsider

398
00:22:07,520 --> 00:22:11,119
Speaker 1: hacking into the device that's it's it's attuned with my

399
00:22:11,240 --> 00:22:15,200
Speaker 1: neural pixie dust and essentially hacking my brain, hacking my

400
00:22:15,200 --> 00:22:19,000
Speaker 1: my memory, even hacking my my perception of reality or

401
00:22:19,080 --> 00:22:22,000
Speaker 1: my my actual consciousness. Yeah, now, of course this is

402
00:22:22,080 --> 00:22:24,000
Speaker 1: this is all predicated on the idea that we have

403
00:22:24,280 --> 00:22:27,199
Speaker 1: a much better idea of how the brain works in

404
00:22:27,240 --> 00:22:30,760
Speaker 1: twenty or thirty years, and we have a kind of technology,

405
00:22:30,800 --> 00:22:35,040
Speaker 1: technology that's so pervasive that people would have access to

406
00:22:35,119 --> 00:22:37,919
Speaker 1: these systems. But what if all of that came together

407
00:22:38,600 --> 00:22:41,320
Speaker 1: and we all had neural pixie dust spread throughout our

408
00:22:41,359 --> 00:22:44,080
Speaker 1: brains to help us, right, to help us remember things,

409
00:22:44,080 --> 00:22:46,840
Speaker 1: to help us do things. And I could say, go

410
00:22:47,040 --> 00:22:49,520
Speaker 1: and hack the system for you and say I would

411
00:22:49,600 --> 00:22:53,160
Speaker 1: really love for Robert to do ten cart wheels today.

412
00:22:53,400 --> 00:22:57,240
Speaker 1: And who knows that you should get that sophisticated. Um.

413
00:22:57,359 --> 00:22:59,920
Speaker 1: But that's a possibility. I mean, that's that's the wonderful

414
00:23:00,200 --> 00:23:02,719
Speaker 1: and the horrific thing about this kind of technology, as

415
00:23:02,760 --> 00:23:05,760
Speaker 1: you can really go off the deep end, um and

416
00:23:05,840 --> 00:23:07,560
Speaker 1: kind of go to the dark side and imagine all

417
00:23:07,600 --> 00:23:12,280
Speaker 1: of these things. Of course, we know that science has

418
00:23:12,320 --> 00:23:15,160
Speaker 1: a lot of integrity and that technology so far has

419
00:23:15,200 --> 00:23:19,960
Speaker 1: been uh not used really that much for ill will.

420
00:23:20,480 --> 00:23:23,560
Speaker 1: But oh no, I can't think of a single example

421
00:23:23,640 --> 00:23:28,160
Speaker 1: of technology being used to hurt anyone. Um. Well there, yes,

422
00:23:28,240 --> 00:23:29,800
Speaker 1: you have a point there, But I guess what I'm

423
00:23:29,800 --> 00:23:31,880
Speaker 1: saying is that I so far have not been able

424
00:23:31,920 --> 00:23:35,159
Speaker 1: to pick up on the black market yet an exco

425
00:23:35,320 --> 00:23:38,960
Speaker 1: exo skeleton that can sort of let me U rampage

426
00:23:38,960 --> 00:23:41,919
Speaker 1: through the streets of Atlanta. It's true. So you know,

427
00:23:42,000 --> 00:23:45,560
Speaker 1: maybe this will be a technological advancement that society will

428
00:23:45,600 --> 00:23:48,520
Speaker 1: be able to stay abreast off instead of letting it

429
00:23:48,560 --> 00:23:51,720
Speaker 1: get ahead of of of society as we often do

430
00:23:52,200 --> 00:23:55,320
Speaker 1: with technological advancements. That would be the hope. But here's

431
00:23:55,320 --> 00:23:58,160
Speaker 1: something I want to say to any kids listening. If

432
00:23:58,200 --> 00:24:01,920
Speaker 1: you guys are interested in neuros science, um, and you're

433
00:24:01,960 --> 00:24:05,520
Speaker 1: also interested in engineering or even chemistry or mathematic mathematics,

434
00:24:05,520 --> 00:24:09,960
Speaker 1: you might want to consider joining up with neuroscientists on

435
00:24:10,040 --> 00:24:12,920
Speaker 1: this because, as they were saying on the panel, they

436
00:24:13,000 --> 00:24:16,240
Speaker 1: need people who are coming from these different disciplines, even

437
00:24:16,280 --> 00:24:20,440
Speaker 1: like physics, to help figure out how some of these

438
00:24:20,440 --> 00:24:23,320
Speaker 1: technologies work with the brain and how best to do this.

439
00:24:23,480 --> 00:24:27,720
Speaker 1: It's not just neuroscientists figuring out. They need other disciplines

440
00:24:27,800 --> 00:24:31,439
Speaker 1: to really make this work. Yeah, otherwise, how are we

441
00:24:31,480 --> 00:24:34,000
Speaker 1: going to get to the point where you can actually

442
00:24:34,600 --> 00:24:38,159
Speaker 1: download a copy of your brain and have that stored

443
00:24:38,200 --> 00:24:40,760
Speaker 1: away potentially for safe keeping, and we could reach the

444
00:24:40,800 --> 00:24:44,760
Speaker 1: point where, um, I instantly think of the book Altered

445
00:24:44,800 --> 00:24:48,840
Speaker 1: Carbon by writer Richard K. Morrigan, in which you have

446
00:24:48,880 --> 00:24:52,439
Speaker 1: individuals who have are certainly individuals who can afford it,

447
00:24:53,040 --> 00:24:56,920
Speaker 1: the super rich, for instance, who have copies of their

448
00:24:56,960 --> 00:25:00,320
Speaker 1: brain backed up so that if something happens to their body,

449
00:25:00,440 --> 00:25:02,840
Speaker 1: they can just have their mind re sleeved into a

450
00:25:02,840 --> 00:25:06,760
Speaker 1: new body. Or you want to explore another planet, you

451
00:25:06,840 --> 00:25:10,119
Speaker 1: just have you a copy of your mind sent, uh,

452
00:25:10,440 --> 00:25:12,320
Speaker 1: you know, at the speed of light to to this

453
00:25:12,400 --> 00:25:14,440
Speaker 1: location where it can be put into a new body

454
00:25:14,600 --> 00:25:17,359
Speaker 1: or a robotic body to do whatever needs to be done. Yeah,

455
00:25:17,400 --> 00:25:20,560
Speaker 1: but before all this happens again, they they need to

456
00:25:20,600 --> 00:25:22,920
Speaker 1: put the rubber to the road here and figure out

457
00:25:22,960 --> 00:25:25,719
Speaker 1: how the brain works. Also, technology they need to upgrade

458
00:25:25,720 --> 00:25:28,160
Speaker 1: here because you know, using fMRI I too to look

459
00:25:28,160 --> 00:25:31,560
Speaker 1: at these neuronal spikes, it's not a perfect science because

460
00:25:31,720 --> 00:25:34,480
Speaker 1: the neuronal neuronal activity is happening at something like one

461
00:25:34,680 --> 00:25:37,600
Speaker 1: thousands of a second, but the imaging itself is only

462
00:25:37,640 --> 00:25:40,199
Speaker 1: happening it one second, so it's not a true picture

463
00:25:40,240 --> 00:25:43,240
Speaker 1: of the brain and what's happening. Right, But through this technology,

464
00:25:43,240 --> 00:25:46,239
Speaker 1: well we were conceivably having much better idea of what

465
00:25:46,280 --> 00:25:49,200
Speaker 1: the brain is doing. Yeah, and and Maha Ribbits said

466
00:25:49,440 --> 00:25:51,640
Speaker 1: that where we are with the brain is a lot

467
00:25:51,760 --> 00:25:57,120
Speaker 1: like where we were with cosmology right before the Hubble telescope,

468
00:25:57,280 --> 00:26:00,600
Speaker 1: right and and just like the Hubble telescope allowed us

469
00:26:00,680 --> 00:26:05,440
Speaker 1: to really peer into uh into space and get an

470
00:26:05,520 --> 00:26:08,120
Speaker 1: idea of the breadth and depth of it. He suing,

471
00:26:08,240 --> 00:26:11,600
Speaker 1: there are technologies coming online now that will allow us

472
00:26:11,600 --> 00:26:13,280
Speaker 1: to do the same thing with a brain that middle

473
00:26:13,320 --> 00:26:16,919
Speaker 1: space that we don't know much about now. You know,

474
00:26:16,960 --> 00:26:19,359
Speaker 1: you're talking about hacking earlier, the idea of someone hacking

475
00:26:19,359 --> 00:26:23,520
Speaker 1: into your personal thoughts in India, the inner workings your brain. Um,

476
00:26:24,440 --> 00:26:26,920
Speaker 1: I can't help it, wonder So, so right now we're

477
00:26:27,400 --> 00:26:30,480
Speaker 1: as we as a civilization or we as a culture anyway,

478
00:26:30,480 --> 00:26:34,960
Speaker 1: are obsessed with the lives of celebrities and or not

479
00:26:34,960 --> 00:26:39,120
Speaker 1: even necessarily celebrities, but sort of the the reality stars

480
00:26:39,480 --> 00:26:41,760
Speaker 1: just some sort of ordinary persons thrust in the limelighte.

481
00:26:41,760 --> 00:26:44,080
Speaker 1: We get to watch everything they're doing. But imagine a

482
00:26:44,119 --> 00:26:46,639
Speaker 1: future where it would maybe not even be there just

483
00:26:46,720 --> 00:26:49,960
Speaker 1: their lives, but their actual thoughts we could tap into.

484
00:26:50,760 --> 00:26:52,920
Speaker 1: Or someone's a great thinker, perhaps their thoughts would be

485
00:26:52,960 --> 00:26:56,240
Speaker 1: considered art thought art And for that matter, then is

486
00:26:56,280 --> 00:26:58,800
Speaker 1: there such thing as hate thought? Could you be prosecutecuted

487
00:26:58,840 --> 00:27:01,720
Speaker 1: for hate thought? Well, that's the idea, right, because all

488
00:27:01,760 --> 00:27:04,480
Speaker 1: of a sudden you have would have access to neuronal

489
00:27:04,600 --> 00:27:07,280
Speaker 1: activity that would point to your subconscious. And we know

490
00:27:07,320 --> 00:27:09,960
Speaker 1: the subconscious is all it's down under there making all

491
00:27:09,960 --> 00:27:13,359
Speaker 1: of these diabolical decisions for us, things that we aren't

492
00:27:13,359 --> 00:27:15,959
Speaker 1: really aware of until it reaches the surface of consciousness.

493
00:27:15,960 --> 00:27:18,879
Speaker 1: So if you were able to peer into your subconscious

494
00:27:19,720 --> 00:27:24,000
Speaker 1: via this technology, would you become super aware, a really

495
00:27:24,040 --> 00:27:27,119
Speaker 1: self aware of person, or would you become super paranoid

496
00:27:27,200 --> 00:27:31,800
Speaker 1: that you know, someone was able to predict your movements

497
00:27:31,880 --> 00:27:34,639
Speaker 1: before you could even complete that thought. Yeah, what if

498
00:27:34,680 --> 00:27:39,080
Speaker 1: Google ends up selling your subconscious to advertisers in the future.

499
00:27:39,200 --> 00:27:41,160
Speaker 1: What if hackers what have you turned in the news

500
00:27:41,200 --> 00:27:44,800
Speaker 1: one day? Sorry, hackers got into Amazon and they stole

501
00:27:44,840 --> 00:27:48,080
Speaker 1: everybody's subconscious and their credit cards. But don't worry, we'll

502
00:27:48,119 --> 00:27:51,679
Speaker 1: monitor your credit card for a full year. Dark stuff,

503
00:27:51,720 --> 00:27:55,520
Speaker 1: my friend. But fascinating and wonderful in the current abilities

504
00:27:55,560 --> 00:27:59,920
Speaker 1: to actually help people who need that bridge from a tocy. Yeah,

505
00:28:00,080 --> 00:28:02,359
Speaker 1: and I mean that's the bottom line here, looking at,

506
00:28:02,440 --> 00:28:06,480
Speaker 1: especially in the near term, ways that this technology can

507
00:28:06,560 --> 00:28:09,920
Speaker 1: drastically improve the lives of people who need to have

508
00:28:10,480 --> 00:28:13,640
Speaker 1: uh that that gap bridge that need links in their

509
00:28:13,720 --> 00:28:18,959
Speaker 1: neural chain reforged. Alright, So there you have a neural

510
00:28:19,119 --> 00:28:22,040
Speaker 1: pixie dust. I'm sure everyone has some thoughts on this

511
00:28:22,040 --> 00:28:26,560
Speaker 1: particular topic, be it the the near term health applications

512
00:28:26,600 --> 00:28:29,919
Speaker 1: that we're talking about or the long term science fiction

513
00:28:30,000 --> 00:28:32,640
Speaker 1: e stuff. Uh. Either way, we'd love to hear from

514
00:28:32,680 --> 00:28:35,080
Speaker 1: you with your inside on this um. You can reach

515
00:28:35,160 --> 00:28:36,879
Speaker 1: us out to us in a number of different ways.

516
00:28:37,119 --> 00:28:38,800
Speaker 1: As always, go to stuff to bow your mind dot com.

517
00:28:38,800 --> 00:28:40,760
Speaker 1: That's the mother ship. That's where you will find all

518
00:28:40,800 --> 00:28:43,040
Speaker 1: of our blog post, all of our podcasts, all our videos,

519
00:28:43,080 --> 00:28:45,200
Speaker 1: links out to our social media accounts, and hey be

520
00:28:45,240 --> 00:28:48,160
Speaker 1: sure to go to YouTube where we are mind Stuff Show.

521
00:28:48,360 --> 00:28:50,960
Speaker 1: Check out all of our video series, including this fabulous

522
00:28:50,960 --> 00:28:55,080
Speaker 1: new Elevator series. It is wonderful quirky stuff. Um as I,

523
00:28:55,120 --> 00:28:58,280
Speaker 1: as I think I was explained to to somebody recently

524
00:28:58,600 --> 00:29:01,120
Speaker 1: our video projects, I feel like what we we're trying

525
00:29:01,160 --> 00:29:04,240
Speaker 1: to do different scoops of ice cream for different viewers.

526
00:29:04,320 --> 00:29:08,840
Speaker 1: So whereas the Monster Science series might be a bit

527
00:29:09,160 --> 00:29:13,560
Speaker 1: to um cotton candy and gummy worms for some listeners. Uh,

528
00:29:13,640 --> 00:29:17,000
Speaker 1: some viewers rather uh the the the the information Elevator,

529
00:29:17,040 --> 00:29:20,080
Speaker 1: I feel like it's more what's a refined but creative

530
00:29:20,400 --> 00:29:27,160
Speaker 1: ice cream flavor? M m, maybe a lemon timebet yeah yeah,

531
00:29:27,400 --> 00:29:28,960
Speaker 1: Or I had this one the other day, an actual

532
00:29:28,960 --> 00:29:33,280
Speaker 1: ice cream. It was dates and the salmon vinegar. Uh yeah.

533
00:29:33,280 --> 00:29:35,640
Speaker 1: But now I feel like you're like, we're talking about

534
00:29:35,640 --> 00:29:39,880
Speaker 1: park Avenue here. Yeah, yeah, park Avenue. We can some

535
00:29:39,880 --> 00:29:42,160
Speaker 1: of our stuff is Park Avenue. Monster Science is not

536
00:29:42,200 --> 00:29:43,800
Speaker 1: Park Avenu. This is sort of like I feel like

537
00:29:43,840 --> 00:29:46,960
Speaker 1: Elevator information elevator might be. This is sort of like

538
00:29:47,560 --> 00:29:50,160
Speaker 1: someone who might hang out in your elevator on Park

539
00:29:50,160 --> 00:29:53,160
Speaker 1: Avenue and they're not really paid to be there, but

540
00:29:53,240 --> 00:29:57,800
Speaker 1: they hang out there. That's that's perfect. And they stopped. Yeah, yeah,

541
00:29:57,800 --> 00:29:59,480
Speaker 1: thanks for the call. Yeah, and guys, if you haven't

542
00:29:59,560 --> 00:30:01,320
Speaker 1: checked out moll the scientists got to do it because

543
00:30:01,320 --> 00:30:04,239
Speaker 1: it is a wonderful like gummy worm packed full of

544
00:30:04,880 --> 00:30:09,560
Speaker 1: ice cream, super normal stimuli shot of science. That's right,

545
00:30:09,600 --> 00:30:12,000
Speaker 1: that's what that's what we're trying to do to all right. Uh,

546
00:30:12,120 --> 00:30:13,880
Speaker 1: you guys have thoughts, We want to hear them and

547
00:30:13,960 --> 00:30:16,280
Speaker 1: you can send them to blow the mind at how

548
00:30:16,360 --> 00:30:23,640
Speaker 1: stuff works dot com for more on this and thousands

549
00:30:23,640 --> 00:30:32,320
Speaker 1: of other topics. Is it how stuff works dot com